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Decision Support Based on Time-Series Analytics: A Cluster Methodology

  • Wanli Xing
  • Rui Guo
  • Nathan Lowrance
  • Thomas Kochtanek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8522)

Abstract

Web analytic techniques have become increasingly popular, particularly Google Analytics time-series dashboards. But interpretations of a website’s visits traffic data may be oversimplified and limited by Google Analytics existing functionalities. This means website mangers have to make estimations rather than mathematically informed decisions. In order to gain a more precise view of longitudinal website visits traffic data, the researchers mathematically transformed the existing Goggle Analytics’ log data allowing the vectors of website visits per each year to be considered simultaneously. The methodology groups the data of an example website gathered over an ‘x’ year period into ‘y’ clusters of data. The results show that the transformed data is richer, more accurate and informative, potentially allowing website managers to make more informed decisions concerning promoting, developing, and maintaining their websites rather than relying on estimations.

Keywords

Temporal analytics Google analytics cluster analysis decision support website management 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Wanli Xing
    • 1
  • Rui Guo
    • 2
  • Nathan Lowrance
    • 1
  • Thomas Kochtanek
    • 1
  1. 1.School of Information Science and Learning TechnologiesUniversity of MissouriColumbiaUSA
  2. 2.Department of Civil and Environmental EngineeringUniversity of South FloridaTampa, ColumbiaUSA

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